AT-MoE: Adaptive Task-planning Mixture of Experts via LoRA Approach

• URL: http://arxiv.org/abs/2410.10896v2
• Date: Sat, 19 Oct 2024 02:24:07 GMT
• Title: AT-MoE: Adaptive Task-planning Mixture of Experts via LoRA Approach
• Authors: Xurui Li, Juanjuan Yao,
• Abstract summary: This paper introduces the Adaptive Task-planing Mixture of Experts(AT-MoE) architecture. We first train task-specific experts via LoRA approach to enhance problem-solving capabilities and interpretability in specialized areas. We then introduce a layer-wise adaptive grouped routing module that optimize module fusion based on complex task instructions.
• Score: 0.6906005491572401
• License:
• Abstract: The advent of Large Language Models (LLMs) has ushered in a new era of artificial intelligence, with the potential to transform various sectors through automation and insightful analysis. The Mixture of Experts (MoE) architecture has been proposed as a solution to enhance model performance in complex tasks. Yet, existing MoE models struggle with task-specific learning and interpretability, especially in fields like medicine where precision is critical. This paper introduces the Adaptive Task-planing Mixture of Experts(AT-MoE), an innovative architecture designed to address these limitations. We first train task-specific experts via LoRA approach to enhance problem-solving capabilities and interpretability in specialized areas. Subsequently, we introduce a layer-wise adaptive grouped routing module that optimizes module fusion based on complex task instructions, ensuring optimal task resolution. The grouped routing module first perform overall weight allocation from the dimension of the expert group, and then conduct local weight normalization adjustments within the group. This design maintains multi-dimensional balance, controllability, and interpretability, while facilitating task-specific fusion in response to complex instructions.

Related papers

• Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging [111.8456671452411]
  Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. We propose a Weight-Ensembling Mixture of Experts (WEMoE) method for multi-task model merging. We show that WEMoE and E-WEMoE outperform state-of-the-art (SOTA) model merging methods in terms of MTL performance, generalization, and robustness.
  arXiv  Detail & Related papers  (2024-10-29T07:16:31Z)
• Glider: Global and Local Instruction-Driven Expert Router [83.785832410832]
  "Model MoErging" methods prioritize generalization to unseen tasks at the expense of performance on held-in tasks. We propose Global and Local Instruction Driven Expert Router (GLIDER) that integrates a multi-scale routing mechanism. GLIDER achieves substantially improved held-in performance while maintaining strong generalization on held-out tasks.
  arXiv  Detail & Related papers  (2024-10-09T17:59:14Z)
• Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models [16.16372459671255]
  Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget. We propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM. We show that trained routers operate differently from oracles and often yield suboptimal solutions.
  arXiv  Detail & Related papers  (2024-10-01T16:10:21Z)
• Harder Tasks Need More Experts: Dynamic Routing in MoE Models [58.18526590138739]
  We introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models. Our method dynamically selects experts based on the confidence level in expert selection for each input.
  arXiv  Detail & Related papers  (2024-03-12T13:41:15Z)
• Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models [7.966452497550907]
  We propose the Mixture-of-LoRAs (MoA) architecture for multi-task learning with large language models (LLMs) Multiple domain-specific LoRA modules can be aligned with the expert design principles observed in Mixture-of-Experts (MoE) Each LoRA model can be iteratively adapted to a new domain, allowing for quick domain-specific adaptation.
  arXiv  Detail & Related papers  (2024-03-06T03:33:48Z)
• Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning [68.94230363140771]
  Mixture of Cluster-conditional LoRA Experts (MoCLE) MoCLE is a novel Mixture of Experts architecture designed to activate the task-customized model parameters based on the instruction clusters. Experiments on InstructBLIP and LLaVA demonstrate the effectiveness of MoCLE.
  arXiv  Detail & Related papers  (2023-12-19T18:11:19Z)
• JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving [77.51817534090789]
  We propose textbfJiuZhang2.0, a unified Chinese PLM specially for multi-task mathematical problem solving. Our idea is to maintain a moderate-sized model and employ the emphcross-task knowledge sharing to improve the model capacity in a multi-task setting.
  arXiv  Detail & Related papers  (2023-06-19T15:45:36Z)
• Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners [74.92558307689265]
  We propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad') We optimize this matching process during the training of a single model. Experiments on the Taskonomy dataset with 13 vision tasks and the PASCAL-Context dataset with 5 vision tasks show the superiority of our approach.
  arXiv  Detail & Related papers  (2022-12-15T18:59:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.